Natural gas hydrate saturation determination method and device based on Bayesian framework, electronic equipment and storage medium

Abstract

The invention provides a natural gas hydrate saturation determination method and device based on a Bayesian framework, electronic equipment and a storage medium. The method comprises the following steps: acquiring first natural gas hydrate saturation and second natural gas hydrate saturation of a target reservoir; obtaining prior probability distribution corresponding to the saturation degree of the natural gas hydrate, wherein the prior probability distribution is determined based on a geological statistical model; determining a corresponding maximum posterior probability estimated value from the prior probability distribution through a Bayesian system according to the prior probability distribution, the first natural gas hydrate saturation and the second natural gas hydrate saturation, and determining the maximum posterior probability estimated value as the target natural gas hydrate saturation of the target reservoir. The method can determine the saturation of the natural gas hydrate based on the Bayesian framework, improves the accuracy and reliability of saturation calculation of the natural gas hydrate, and can be widely applied to the technical field of oil-gas exploration.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration technology, and in particular to a method, device, electronic device and storage medium for determining natural gas hydrate saturation based on a Bayesian framework. Background Art

[0002] As a potential clean energy source, the exploration and development of natural gas hydrates depends on the calculation of their saturation. Among the related technologies, most use the acoustic velocity method or the resistivity method to calculate natural gas hydrate saturation. However, the acoustic velocity method and the resistivity method each calculate saturation based on different physical properties, and both have limitations. The acoustic velocity method is affected by the complexity of geological structures and the interference of inhomogeneous media, and a single velocity parameter cannot accurately map saturation details. The resistivity method is easily affected by background conductivity fluctuations in highly conductive formations and cannot distinguish between hydrate and free water signals. Therefore, the accuracy and reliability of natural gas hydrate saturation calculations caused by related technologies have been greatly reduced, and cannot meet the calculation accuracy requirements.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method, device, electronic device and storage medium for determining natural gas hydrate saturation based on a Bayesian framework, aiming to improve the calculation accuracy and reliability of natural gas hydrate saturation and reduce the error impact caused by a single natural gas hydrate saturation calculation method.

[0005] To achieve the above objectives, one aspect of an embodiment of the present application provides a method for determining natural gas hydrate saturation based on a Bayesian framework, the method comprising the following steps: Obtaining a first natural gas hydrate saturation and a second natural gas hydrate saturation of a target reservoir, wherein the first natural gas hydrate saturation is calculated based on an acoustic wave velocity method, and the second natural gas hydrate saturation is calculated based on a resistivity method; Obtaining a priori probability distribution corresponding to natural gas hydrate saturation, wherein the prior probability distribution is determined based on a geostatistical model; By means of a Bayesian system, a corresponding maximum a posteriori probability estimate is determined from the prior probability distribution according to the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation, and the maximum a posteriori probability estimate is determined as the target natural gas hydrate saturation of the target reservoir.

[0006] In some embodiments, obtaining the first natural gas hydrate saturation and the second natural gas hydrate saturation of the target reservoir includes: Acquiring acoustic wave velocity data and resistivity-related data corresponding to the target reservoir, wherein the resistivity-related data includes formation resistivity, porosity, and formation water resistivity; determining the first natural gas hydrate saturation according to the acoustic wave velocity data and an equivalent medium model; The second natural gas hydrate saturation is determined based on the resistivity-related data and the Archie calculation model.

[0007] In some embodiments, determining, by a Bayesian system, a corresponding maximum a posteriori probability estimate from the prior probability distribution based on the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation includes: constructing a corresponding first likelihood function according to the first natural gas hydrate saturation and the prior probability distribution; constructing a corresponding second likelihood function according to the second natural gas hydrate saturation and the prior probability distribution; Determining a posterior probability distribution based on the first likelihood function, the second likelihood function, and the prior probability distribution; The posterior probability distribution is maximized to determine a maximized posterior probability, and the maximum posterior probability estimate corresponding to the maximized posterior probability is determined from the prior probability distribution.

[0008] In some embodiments, determining the posterior probability distribution based on the first likelihood function, the second likelihood function, and the prior probability distribution includes: Determining a corresponding joint probability according to the first likelihood function, the second likelihood function, and the prior probability distribution by a numerical integration method; Based on the Bayesian formula, the posterior probability distribution is determined according to the first likelihood function, the second likelihood function, the prior probability distribution, and the joint probability.

[0009] In some embodiments, the first likelihood function and the second likelihood function are calculated by the following formula: ; ; ; in, is the first likelihood function, is the first natural gas hydrate saturation, is the first preset measurement error standard deviation based on the acoustic wave velocity method, is an exponential function, is the second likelihood function, is the second natural gas hydrate saturation, is the second preset measurement error standard deviation based on the resistivity method, is the maximum value in the prior probability distribution, is the minimum value in the prior probability distribution, is the prior probability distribution of natural gas hydrate saturation, Indicates that in the interval The gas hydrate saturation is uniformly distributed inside.

[0010] In some embodiments, the joint probability is calculated by the following formula: ; ; in, is the joint probability, is the first likelihood function, is the second likelihood function, is the maximum value in the prior probability distribution, is the minimum value in the prior probability distribution, is the prior probability distribution of natural gas hydrate saturation, Indicates that in the interval The gas hydrate saturation is uniformly distributed inside the gas hydrate. is the first natural gas hydrate saturation, is the second natural gas hydrate saturation.

[0011] In some embodiments, the posterior probability distribution is calculated by the following formula: ; in, is the posterior probability distribution, is the first likelihood function, is the second likelihood function, is the joint probability, is the prior probability distribution of natural gas hydrate saturation.

[0012] To achieve the above objectives, another aspect of the present application provides a device for determining natural gas hydrate saturation based on a Bayesian framework, the device comprising: A first module is configured to obtain a first natural gas hydrate saturation and a second natural gas hydrate saturation of a target reservoir, wherein the first natural gas hydrate saturation is calculated based on an acoustic wave velocity method, and the second natural gas hydrate saturation is calculated based on a resistivity method; The second module is used to obtain a priori probability distribution corresponding to the natural gas hydrate saturation, wherein the priori probability distribution is determined based on a geostatistical model; The third module is used to determine the corresponding maximum a posteriori probability estimate from the prior probability distribution based on the prior probability distribution, the first natural gas hydrate saturation and the second natural gas hydrate saturation through a Bayesian system, and determine the maximum a posteriori probability estimate as the target natural gas hydrate saturation of the target reservoir.

[0013] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method when executing the computer program.

[0014] To achieve the above objectives, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0015] The embodiments of the present application include at least the following beneficial effects: The present application provides a method, apparatus, electronic device, and storage medium for determining natural gas hydrate saturation based on a Bayesian framework. This solution obtains a first natural gas hydrate saturation, a second natural gas hydrate saturation, and a prior probability distribution corresponding to the natural gas hydrate saturation of the target reservoir, and then determines the target natural gas hydrate saturation of the target reservoir based on the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation through a Bayesian system. Based on the Bayesian framework, the present application can integrate the natural gas hydrate saturations obtained based on the acoustic velocity method and the resistivity method, effectively overcoming the limitations of a single saturation calculation method, achieving the determination of natural gas hydrate saturation, improving the accuracy and reliability of natural gas hydrate saturation calculation, and being applicable to different geological conditions and data quality conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a method for determining natural gas hydrate saturation based on a Bayesian framework provided in an embodiment of the present application; Figure 2 yes Figure 1 Flowchart of step S103 in FIG. Figure 3 Schematic diagram of the structure of a natural gas hydrate saturation determination device based on a Bayesian framework provided in an embodiment of the present application; Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0018] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0019] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0021] Among the related technologies, the acoustic velocity method has many shortcomings in practical applications. The complexity of geological structures has a significant negative impact on measurement accuracy, especially in areas with developed faults, where the propagation path of seismic waves is disturbed, resulting in distortion of velocity information. The heterogeneity of the formation also poses a challenge to the acoustic velocity method. The heterogeneity of rock composition, pore structure and fluid distribution makes it difficult to simply attribute changes in acoustic velocity to changes in hydrate saturation, which increases the uncertainty and difficulty of interpretation of the results. The anisotropic effect of formation anisotropy on velocity causes differences in the measured values of acoustic velocity in different directions. If effective correction is not performed, it will directly affect the calculation results of hydrate saturation. It is difficult to accurately map the details of saturation with a single velocity parameter. It is difficult to fully characterize the subtle changes in hydrate saturation based on velocity data alone, which limits the application of this method in fine exploration.

[0022] The resistivity method also has limitations. Changes in formation water salinity have a significant impact on measurement results. Highly salinized formation water will enhance formation conductivity, making it difficult for the resistivity method to accurately identify the filling of hydrates, leading to an overestimation or underestimation of hydrate saturation. The accuracy of the resistivity method decreases in highly conductive formations because the presence of highly conductive minerals or fluids will interfere with the resistivity signal, distorting the measurement results and making it difficult to accurately reflect the true saturation of hydrates. The difficulty in distinguishing between free water and hydrate signals should not be ignored. The free water and hydrates in the pores have similar mechanisms of influence on resistivity. Relying solely on resistivity data, it is difficult to accurately distinguish the contributions of the two to resistivity changes, thereby reducing the accuracy of the method.

[0023] In view of this, an embodiment of the present application provides a method, device, electronic device and storage medium for determining natural gas hydrate saturation based on a Bayesian framework. This solution effectively integrates the advantages of the two methods through the Bayesian framework to achieve more accurate and reliable natural gas hydrate saturation calculation. Specifically, it fully utilizes the sensitivity of the acoustic wave velocity method in rock physical properties and the advantages of the resistivity method in fluid property detection. The data of the two methods are deeply integrated through the Bayesian system to improve the calculation accuracy of natural gas hydrate saturation while reducing the error impact of a single method.

[0024] The embodiment of the present application provides a method for determining the saturation of natural gas hydrates based on a Bayesian framework, which relates to the field of information technology. The method for determining the saturation of natural gas hydrates based on a Bayesian framework provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the method for determining the saturation of natural gas hydrates based on a Bayesian framework, etc., but is not limited to the above forms.

[0025] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0026] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0027] Figure 1 This is an optional flow chart of a method for determining natural gas hydrate saturation based on a Bayesian framework provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S103.

[0028] Step S101, obtaining a first natural gas hydrate saturation and a second natural gas hydrate saturation of a target reservoir, wherein the first natural gas hydrate saturation is calculated based on an acoustic wave velocity method, and the second natural gas hydrate saturation is calculated based on a resistivity method; Step S102, obtaining a priori probability distribution corresponding to natural gas hydrate saturation; Step S103, using a Bayesian system, based on the prior probability distribution, the first gas hydrate saturation, and the second gas hydrate saturation, determining a corresponding maximum a posteriori probability estimate from the prior probability distribution, and determining the maximum a posteriori probability estimate as the target gas hydrate saturation of the target reservoir; Steps S101 to S103 shown in the embodiment of the present application are based on a Bayesian framework to construct a Bayesian system. The Bayesian system can provide uncertainty estimation and evaluate the reliability of the calculation results. Through the Bayesian system, the first natural gas hydrate saturation and the second natural gas hydrate saturation are integrated to perform a joint calculation to determine the final target natural gas hydrate saturation, thereby overcoming the limitations of the single saturation calculation method and improving the accuracy and reliability of the natural gas hydrate saturation calculation.

[0029] In some embodiments, a priori probability distribution is determined based on a geostatistical model or historical data. Optionally, the priori probability obeys a uniform distribution, and the priori probability interval corresponding to the priori probability distribution is ,in, is the minimum value in the prior probability distribution, that is, the minimum value of natural gas hydrate saturation, is the maximum value in the prior probability distribution, that is, the maximum value of natural gas hydrate saturation.

[0030] In some embodiments, step S101 may include but is not limited to steps S201 to S203.

[0031] Step S201, obtaining acoustic wave velocity data and resistivity-related data corresponding to the target reservoir, where the resistivity-related data includes formation resistivity, porosity, and formation water resistivity, and the acoustic wave velocity data includes compressional wave velocity, porosity, and shear wave velocity; Step S202, determining a first natural gas hydrate saturation based on the acoustic wave velocity data and the equivalent medium model; Step S203: determining the second natural gas hydrate saturation based on the resistivity-related data and the Archie calculation model.

[0032] In some embodiments, the first natural gas hydrate saturation is calculated by the following formula: ; in, is the first natural gas hydrate saturation, with a value range of [0,1], where 1 means the pores are completely filled with hydrates. is the longitudinal wave velocity, is the P-wave velocity in the absence of hydrates, which corresponds to the P-wave velocity when the pores contain only water (or gas), and is calculated using a porosity model (such as the Wyllie equation). is the P-wave velocity when the hydrate is fully saturated, corresponding to the pores being completely filled with hydrates ( ) is calculated using an equivalent medium model (such as the BGLT model).

[0033] In some embodiments, the second natural gas hydrate saturation is calculated by the following formula: ; in, is the second natural gas hydrate saturation, is the formation resistivity, obtained through resistivity logging, is the formation water resistivity, obtained through water sample analysis or adjacent well data. is the porosity, which is calculated by neutron logging or density logging. 、 、 These are the corresponding Archie parameters in the Archie calculation model, usually default 、 , in specific applications, 、 、 The specific value of is determined through core test and is not limited.

[0034] In some embodiments, reference Figure 2 , step S103 may include but is not limited to steps S301 to S304.

[0035] Step S301, constructing a corresponding first likelihood function according to the first natural gas hydrate saturation and the prior probability distribution; Step S302: constructing a corresponding second likelihood function according to the second natural gas hydrate saturation and the prior probability distribution; Step S303, determining a posterior probability distribution based on the first likelihood function, the second likelihood function, and the prior probability distribution; Step S304 , performing maximization processing on the posterior probability distribution to determine the maximized posterior probability, and determining a maximum posterior probability estimate corresponding to the maximized posterior probability from the prior probability distribution.

[0036] In some embodiments, the likelihood function is related to the parameter or function, indicating that the current data is observed under given parameters The probability density of is used to construct the first likelihood function and the second likelihood parameter through the Bayesian system.

[0037] In some embodiments, the first likelihood function and the second likelihood function are calculated using the following formula: ; ; ; in, is the first likelihood function, is the first natural gas hydrate saturation, is the first preset measurement error standard deviation based on the acoustic wave velocity method, is an exponential function, is the second likelihood function, is the second natural gas hydrate saturation, is the second preset measurement error standard deviation based on the resistivity method, is the prior probability distribution of natural gas hydrate saturation, Indicates that in the interval The gas hydrate saturation is uniformly distributed inside.

[0038] In step S303 of some embodiments, optionally, a corresponding joint probability is determined according to the first likelihood function, the second likelihood function and the prior probability distribution through a numerical integration method; and based on the Bayes' Theorem, a posterior probability is determined according to the first likelihood function, the second likelihood function, the prior probability distribution and the joint probability.

[0039] In some embodiments, the above joint probability is calculated by the following formula: ; in, is the joint probability.

[0040] In some embodiments, the posterior probability distribution is calculated as follows: ; in, is the posterior probability distribution.

[0041] In step S304 of some embodiments, the derivative maximization of the above-mentioned posterior probability distribution is performed through a Bayesian system to determine the maximum posterior probability estimate corresponding to the maximized posterior probability. Specifically, the Bayesian system is used to solve the zero point of the derivative of the posterior probability distribution to determine the maximized posterior probability. The maximized posterior probability is the optimal balance point under the joint action of the prior and the likelihood. The parameter value corresponding to the maximized posterior probability is determined from the prior probability distribution, that is, the above-mentioned maximum posterior probability estimate.

[0042] See also Figure 3 The embodiment of the present application further provides a natural gas hydrate saturation determination device based on a Bayesian framework, which can implement the above method, and the device includes: The first module is used to obtain a first natural gas hydrate saturation and a second natural gas hydrate saturation of the target reservoir, wherein the first natural gas hydrate saturation is calculated based on the acoustic wave velocity method, and the second natural gas hydrate saturation is calculated based on the resistivity method; The second module is used to obtain the prior probability distribution corresponding to the natural gas hydrate saturation, and the prior probability distribution is determined based on the geostatistical model; The third module is used to determine a corresponding maximum a posteriori probability estimate from the prior probability distribution based on the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation through a Bayesian system, and determine the maximum a posteriori probability estimate as the target natural gas hydrate saturation of the target reservoir.

[0043] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0044] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0045] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0046] See also Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes: The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application. The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the above-mentioned methods of the embodiments of this application. Input / output interface 903, used to implement information input and output; Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.); Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 ); The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0047] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above method is implemented.

[0048] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0049] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0050] It is understandable that the contents of the above method embodiments are all applicable to the present program product embodiments, the functions specifically implemented by the present program product embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0051] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0052] The embodiments of the present application provide a method, device, electronic device, storage medium, and program product for determining natural gas hydrate saturation based on a Bayesian framework. These methods obtain a first natural gas hydrate saturation, a second natural gas hydrate saturation, and a prior probability distribution corresponding to the natural gas hydrate saturation of the target reservoir, and then determine the target natural gas hydrate saturation of the target reservoir using a Bayesian system based on the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation. Based on a Bayesian framework, the present application can integrate natural gas hydrate saturations obtained based on the acoustic velocity method and the resistivity method, effectively overcoming the limitations of a single saturation calculation method, achieving determination of natural gas hydrate saturation, and improving the accuracy and reliability of natural gas hydrate saturation calculations. The method is applicable to different geological conditions and data quality conditions, and can effectively improve the reliability of natural gas hydrate saturation calculations, especially for complex geological structures and highly conductive formations.

[0053] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0054] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0055] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0056] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0057] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0059] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0060] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0062] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0063] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for determining natural gas hydrate saturation based on a Bayesian framework, characterized in that: The method comprises the following steps: Obtaining a first natural gas hydrate saturation and a second natural gas hydrate saturation of a target reservoir, wherein the first natural gas hydrate saturation is calculated based on an acoustic wave velocity method, and the second natural gas hydrate saturation is calculated based on a resistivity method; Obtaining a priori probability distribution corresponding to natural gas hydrate saturation, wherein the prior probability distribution is determined based on a geostatistical model; By means of a Bayesian system, a corresponding maximum a posteriori probability estimate is determined from the prior probability distribution according to the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation, and the maximum a posteriori probability estimate is determined as the target natural gas hydrate saturation of the target reservoir.

2. The method according to claim 1, characterized in that The obtaining of the first natural gas hydrate saturation and the second natural gas hydrate saturation of the target reservoir comprises: Acquiring acoustic wave velocity data and resistivity-related data corresponding to the target reservoir, wherein the resistivity-related data includes formation resistivity, porosity, and formation water resistivity; determining the first natural gas hydrate saturation according to the acoustic wave velocity data and an equivalent medium model; The second natural gas hydrate saturation is determined based on the resistivity-related data and the Archie calculation model.

3. The method according to claim 1, characterized in that The determining, by a Bayesian system, a corresponding maximum a posteriori probability estimate from the prior probability distribution according to the prior probability distribution, the first natural gas hydrate saturation, and the second natural gas hydrate saturation includes: constructing a corresponding first likelihood function according to the first natural gas hydrate saturation and the prior probability distribution; constructing a corresponding second likelihood function according to the second natural gas hydrate saturation and the prior probability distribution; Determining a posterior probability distribution based on the first likelihood function, the second likelihood function, and the prior probability distribution; The posterior probability distribution is maximized to determine a maximized posterior probability, and the maximum posterior probability estimate corresponding to the maximized posterior probability is determined from the prior probability distribution.

4. The method according to claim 3, characterized in that Determining the posterior probability distribution according to the first likelihood function, the second likelihood function, and the prior probability distribution includes: Determining a corresponding joint probability according to the first likelihood function, the second likelihood function, and the prior probability distribution by a numerical integration method; Based on the Bayesian formula, the posterior probability distribution is determined according to the first likelihood function, the second likelihood function, the prior probability distribution, and the joint probability.

5. The method according to claim 3, characterized in that The first likelihood function and the second likelihood function are calculated by the following formula: ； ； ； in, is the first likelihood function, is the first natural gas hydrate saturation, is the first preset measurement error standard deviation based on the acoustic wave velocity method, is an exponential function, is the second likelihood function, is the second natural gas hydrate saturation, is the second preset measurement error standard deviation based on the resistivity method, is the maximum value in the prior probability distribution, is the minimum value in the prior probability distribution, is the prior probability distribution of natural gas hydrate saturation, Indicates that in the interval The gas hydrate saturation is uniformly distributed inside.

6. The method according to claim 4, characterized in that The joint probability is calculated by the following formula: ； ； in, is the joint probability, is the first likelihood function, is the second likelihood function, is the maximum value in the prior probability distribution, is the minimum value in the prior probability distribution, is the prior probability distribution of natural gas hydrate saturation, Indicates that in the interval The gas hydrate saturation is uniformly distributed inside the is the first natural gas hydrate saturation, is the second natural gas hydrate saturation.

7. The method according to any one of claims 4 to 6, characterized in that The posterior probability distribution is calculated by the following formula: ； in, is the posterior probability distribution, is the first likelihood function, is the second likelihood function, is the joint probability, is the prior probability distribution of natural gas hydrate saturation.

8. A natural gas hydrate saturation determination device based on a Bayesian framework, characterized in that: The device comprises: A first module is configured to obtain a first natural gas hydrate saturation and a second natural gas hydrate saturation of a target reservoir, wherein the first natural gas hydrate saturation is calculated based on an acoustic wave velocity method, and the second natural gas hydrate saturation is calculated based on a resistivity method; The second module is used to obtain a priori probability distribution corresponding to the natural gas hydrate saturation, wherein the priori probability distribution is determined based on a geostatistical model; The third module is used to determine the corresponding maximum a posteriori probability estimate from the prior probability distribution based on the prior probability distribution, the first natural gas hydrate saturation and the second natural gas hydrate saturation through a Bayesian system, and determine the maximum a posteriori probability estimate as the target natural gas hydrate saturation of the target reservoir.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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